Machine learning methods forfinancial forecasting and tradingprofitability: Evidence during theRussia–Ukraine war
DOI:
https://doi.org/10.1108/REGE-05-2022-0079Keywords:
Algorithmic trading, Support Vector Machines, Russia-Ukraine war, Efficient market hypothesis, Trading profitabilityAbstract
Purpose – Evaluate the effectiveness of machine learning models to yield profitability over the market benchmark, notably in periods of systemic instability, such as the ongoing war between Russia and Ukraine.
Design/methodology/approach – Computational experiments using Support Vector Machine classifiers to predict stock price movements for three financial markets and construct profitable trading strategies to subsidize investors’ decision-making.
Findings – On average, machine learning models outperformed the market benchmarks during the more volatile period of the Russia-Ukraine war, but not during the period before the conflict. Moreover, the hyperparameter combinations for which the profitability is superior were found to be highly sensitive to small variations during the model training process.
Practical implications – Investors should proceed with caution when applying machine learning models for stock price forecasting and trading recommendation, as their superior performance for volatile periods – in terms of generating abnormal gains over the market – was not observed for a period of relative stability in the economy.
Originality/value – This paper’s approach to search for financial strategies that succeed in outperforming the market provides empirical evidence about the effectiveness of state-of-the-art machine learning techniques before and after the conflict deflagration, being of potential value for researchers on quantitative finance and market professionals who operate in the financial segment.
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